Various imaging methods are available in the medical field for visually displaying the inner structures of bodies being investigated. X-ray imaging, which provides a two-dimensional image of the irradiated structures, has existed for quite some time. Other methods, such as computerized tomography (CT) or magnetic resonance (MR) tomography are also able to provide three-dimensional voxel data sets. The term “voxel” is a combination of the words “volumetric” and “pixel.” In a spatial voxel data set present in discretized form in Cartesian coordinates, a voxel corresponds to an associated discrete value on an XYZ coordinate of the data set. A voxel is thus a three-dimensional equivalent of a pixel. The data contained in a voxel data set are usually scalar variables, for example intensity values or color values, which are determined using volume graphic means for visual imaging.
However, these latter-referenced methods are still costly and time-consuming, or (for CT) also involve high radiation exposure levels. For this reason standard two-dimensional X-ray imaging is often used, even when, in surgical planning, for example, three-dimensional imaging would be desirable. Although these images do not provide depth information, experienced medical specialists are able to visualize the imaged three-dimensional structures.
The object of the invention is to provide a method for visually displaying and/or evaluating measurement data from imaging methods, which allows the measured data to be efficiently evaluated and, in particular, enables volume models to be calculated from the measurement data from two-dimensional recordings.
According to the invention, the method comprises the following steps: a) calculating a parameterized statistical model from example voxel data sets that map different objects of an identical object class; b) carrying out at least one imaging method on an object to be examined of the object class in order to extract actual measurement data; c) adjusting a set of model parameters of the parameterized statistical model; d) determining a difference between the actual measurement data and the parameterized statistical model; e) repeating steps c) and d) while changing the model parameters until the difference between the actual measurement data and the parameterized statistical model is minimal, and f) visually displaying and/or evaluating the statistical model parameterized in said manner. Steps a) through f) are preferably carried out in the referenced sequence.
In other words, in the method for processing measurement data from imaging methods a parameterized statistical model is calculated from example voxel data sets in such a way that data calculated from the model optimally match the measured data, and the model thus obtained is outputted for visual display or further processing.
The method according to the invention allows, among other things, the calculation of volume models from two-dimensional pictures (e.g., X-ray pictures or individual tomographs, for example) by calculating, by use of a statistical model created from example data sets, the most probable configuration which could have resulted in the picture. In this case, statistical information obtained from the example data corresponds to the experience of the medical specialist, who is able to visualize a three-dimensional configuration which has resulted in a given picture. Corresponding techniques have heretofore been used only in the field of modeling of two-dimensional images (T. F. Cootes, G. J. Edwards, and C. J. Taylor. Active appearance models. In Burkhardt and Neumann, editors, Computer Vision—ECCV'98 Vol. II, Freiburg, Germany, 1998. Springer, Lecture Notes in Computer Science 1407) or three-dimensional surface models (morphable models (V. Blanz, T. Vetter. Method and device for the processing of images based on morphable models, EP1039417)), but not for volume models.
One refinement of the method includes the following steps: c1) calculating virtual measurement data from the parameterized statistical model, and d1) determining the difference between the actual measurement data and the parameterized statistical model by determining a difference between the actual measurement data and the virtual measurement data. Step c1) is preferably carried out after step c) and before step d), and step d1) is preferably carried out after step d) and before step e).
In one refinement of the method, the example voxel data sets are extracted from CT and/or MR voxel data.
In one refinement of the method, the actual measurement data are extracted on the basis of one or more X-ray pictures.
In one refinement of the method, the actual measurement data are extracted from not yet back-projected data from one or more CT and/or MR images.
In one refinement of the method, the actual measurement data are extracted from voxel data, for example back-projected data from one or more CT and/or MR images.
In one refinement of the method, from the parameterized statistical model a reference data set is calculated which registers the measurement data with the reference data set, and model parameters are calculated which best represent the model which matches the measurement data.
In one refinement of the method, the parameterized statistical model is obtained from a linear combination of example vectors, a respective example vector being assigned to an associated example voxel data set, and components of the respective example vector describing the position and intensity of volume elements of the associated example voxel data set. The example vectors are determined on the basis of the example voxel data sets. Reparameterization as well as parameter reduction may be performed to determine or calculate the example vectors.
In one refinement of the method, a vector space defined by the example vectors is reparameterized.
In one refinement of the method, the evaluation of the parameterized statistical model in step f) includes detection of anomalies in the actual measurement data.
The method according to the invention or certain substeps of the method may preferably be carried out on specialized hardware, for example on the basis of programmable logical units.